Pervasive metrics

the dashboard provides a collection of pervasive issue analysis metrics that help you understand incident patterns across your managed environment each tile highlights a specific metric, enabling you to monitor incident volumes, identify recurring issues, pinpoint the servers and categories most affected and detect sources of incident noise by analyzing these metrics, teams can focus on recurring problem areas, prioritize remediation efforts and make more informed operational decisions to improve service reliability and reduce incident impact date and time values displayed in the dashboard are based on your browser's configured time zone as a result, the displayed time zone may vary between users viewing the same dashboard metric data sources the pervasive dashboard measures incident activity by comparing the number of incident tickets within scope against the total number of servers associated with an account the metric is calculated using a rolling three month period, providing a consistent view of incident volume and activity over time to generate these insights, the dashboard aggregates data from multiple sources, including incident records, server inventory information and event data the following data sources are used servicenow serves as the primary source of incident data, providing information about incident tickets, including volume, categorization, priority and trends used throughout the dashboard inventory service provides server inventory data, including the total number of managed servers associated with an account this information is sourced from inventory discovery and is used to calculate incident metrics relative to the server population netcool provides event and alert data that helps identify operational issues, recurring patterns and potential sources of incident activity within the environment calculation of projected incident per server per month value describes the use cases provided by this insight the current analytics solution is based on elastic search in scope of kyndryl incident received month to date projected incident per server per month = x number of days of full month elapsed days within the month this insight is measured over last 3 months rolling period use cases incident noise reduction methodology follow these steps to identify recurring sources of incident noise, investigate the underlying causes and define actions to reduce incident volume apply the dashboard filters at the top of the page select the following filtering options created on last 30 days assignment queue optional select ibm to focus on tickets within kyndryl scope select apply to update the insights scroll to top 50 category and identify the top three categories for each of the top three categories select the category to update the other insights review the day wise category trend to understand how ticket volume changes over time review top 50 servers to identify servers contributing significantly to the category and record their ticket counts review incident details for common ticket messages or recurring patterns use search phrases when needed and record the related ticket numbers determine the appropriate action for each recurring issue document the required action for each identified issue preventive action remediation action customer action add the identified actions to a timeline and assign owners to track them through completion measure the impact of the preventive and remediation actions to determine whether they reduced recurring incidents repeat the process for the next top categories that require attention click here https //kyndryl sharepoint com/\ p\ /r/sites/iaiopsdocs/ layouts/15/doc aspx?sourcedoc=%7b0bdec040 ec02 4dae bee7 20b22373a5f7%7d\&file=integrated%20aiops%20 %20best%20practice%20use%20cases pptx\&action=edit\&mobileredirect=true , to explore additional use cases